On-the-Spot Decision-Making System of Basketball Game Based on Data Mining Algorithm

Abstract

Modern competitive basketball has developed into a skilled international competitive sport. The situation in basketball is changing rapidly. At the same time, tactics are one of the main factors affecting game performance in athletic basketball. Whether the athletes successfully use the techniques and tactics in the game or not will often become the decisive factor for the outcome of the game. Therefore, it is essential to grasp the situation of basketball games, to study the technical and tactical rules of basketball competitive games, and to provide scientific support for the daily technical and tactical training of competitive basketball and technical and tactical decision-making during the game, which is an important way to improve the level of basketball competition in China. As a decision support technology, data mining plays an important role in decision support systems. This decision-making method is closely related to the amount of data, the way of organization, and the structure of the organization. Based on this, this study studies the improved Bayesian algorithm classification algorithm in data mining, and it also reorganizes and restructures the large, intricate, and unorganized data from the database of the system according to the decision-making demand. After a series of data processing processes are completed, the latest data organization mode is obtained, and new data correlation information is found from it, by finding the relevance of knowledge in the decision support system of the database-based relational database management system, and presenting the implicit structural information in the data to provide users with more accurate decision-making information. Finally, in the investigation and analysis of the on-site decision-making system of basketball games, it is found that 48% of sport coaches think it is very helpful, indicating that the system has merit.